Meta-analysis of SNP markers associated with milk production traits in Holstein cattle based on Animal QTLdb and GenBank data

This study aimed to identify SNP markers associated with milk yield, fat content, and protein percentage in Holstein cattle through a comprehensive meta-analysis of Animal QTLdb (CattleQTLdb section) and GenBank. A systematic review of 47 independent studies published between 2015 and 2024, covering 23,456 animals, was conducted. In total, 892 unique SNP markers were identified, and pooled effect estimates were calculated for 723 of them using a random-effects model. Heterogeneity analysis, functional annotation, and prioritization of significant variants were performed. The analysis revealed 47 SNPs reaching genome-wide significance (p < 10^–6 ), with the strongest association observed for rs109421300 in the DGAT1 gene, which increased milk fat content by an average of 0.42 % (95 % CI: 0.38–0.46; p = 1.2 × 10 ^–8 ) but reduced yield by 153 kg. Consistent effects were also found for ABCG2, GHR, STAT5A, LEP, and SCD1 genes, with between-population variability ranging from 15 to 25 percent. Functional annotation showed that 67 % of significant SNPs were linked to lipid metabolism and lactation, and enrichment analysis revealed significant enrichment of the lipid biosynthesis category, containing 34 genes (FDR = 2.3 × 10 ^–12 ). Based on prioritization algorithms, a panel of 30 SNPs was proposed for commercial genotyping, compatible with Illumina BovineSNP50 technology, with a testing cost of $25–30 per animal. Modeling demonstrated that the application of this panel could improve the efficiency of genomic selection by 14.7 % (95 % CI: 12.1–17.3 %). These findings provide a solid foundation for enhancing genomic breeding programs in dairy cattle.

1. Alexandratos N., Bruinsma J. World agriculture towards 2030/2050: the 2012 revision. ESA Working Paper, no. 12-03. Rome, FAO, 2012. 154 p.

2. Goddard M. E., Hayes B. J. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 2009, vol. 10, no. 6, pp. 381–391. https://doi.org/10.1038/nrg2575

3. Pryce J. E., Daetwyler H. D. Designing dairy cattle breeding schemes under genomic selection: a review of international research. Animal Production Science, 2012, vol. 52, no. 3, pp. 107–114. https://doi.org/10.1071/AN11098

4. Rosen B. D., Bickhart D. M., Schnabel R. D., Koren S., Elsik C. G., Tseng E. [et al.]. De novo assembly of the cattle reference genome with single-molecule sequencing. GigaScience, 2020, vol. 9, no. 3, art. giaa021. https://doi.org/10.1093/gigascience/giaa021

5. Matukumalli L. K., Lawley C. T., Schnabel R. D., Taylor J. F., Allan M. F., Heaton M. P. [et al.]. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One, 2009, vol. 4, no. 4, art. e5350. https://doi.org/10.1371/journal.pone.0005350

6. Vignal A., Milan D., SanCristobal M., Eggen A. A review on SNP and other types of molecular markers and their use in animal genetics. Genetics Selection Evolution, 2002, vol. 34, no. 3, pp. 275–305. https://doi.org/10.1186/1297-9686-34-3-275

7. Goddard M. E., Hayes B. J., Meuwissen T. H. E. Using the genomic relationship matrix to predict the accuracy of genomic selection. Journal of Animal Breeding and Genetics, 2011, vol. 128, no. 6, pp. 409–421. https://doi.org/10.1111/j.1439-0388.2011.00964.x

8. Visscher P. M., Wray N. R., Zhang Q., Sklar P., McCarthy M. I., Brown M. A., Yang J. 10 years of GWAS discovery: biology, function, and translation. American Journal of Human Genetics, 2017, vol. 101, no. 1, pp. 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005

9. Jiang L., Liu J., Sun D., Ma P., Ding X., Yu Y., Zhang Q. Genome wide association studies for milk production traits in Chinese Holstein population. PLoS One, 2010, vol. 5, no. 10, art. e13661. https://doi.org/10.1371/journal.pone.0013661

10. Pausch H., Emmerling R., Schwarzenbacher H., Fries R. A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle. Genetics Selection Evolution, 2016, vol. 48, art. 14. https://doi.org/10.1186/s12711-016-0190-4

11. De Roos A. P. W., Hayes B. J., Goddard M. E. Reliability of genomic predictions across multiple populations. Genetics, 2009, vol. 183, no. 4, pp. 1545–1553. https://doi.org/10.1534/genetics.109.104935

12. Grisart B., Coppieters W., Farnir F., Karim L., Ford C., Berzi P. [et al.]. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Research, 2002, vol. 12, no. 2, pp. 222–231. https://doi.org/10.1101/gr.224202

13. Grisart B., Farnir F., Karim L., Cambisano N., Kim J. J., Kvasz A., Mni M., Simon P., Frère J. M., Coppieters W., Georges M. Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. PNAS USA, 2004, vol. 101, no. 8, pp. 2398–2403. https://doi.org/10.1073/pnas.0308518100

14. Schennink A., Bovenhuis H., Léon-Kloosterziel K. M., Van Arendonk J. A. M., Visker M. H. P. W. Effect of polymorphisms in the FASN, OLR1, PPARGC1A, PRL and STAT5A genes on bovine milk-fat composition. Animal Genetics, 2009, vol. 40, no. 6, pp. 909–916. https://doi.org/10.1111/j.1365-2052.2009.01940.x

15. Bovenhuis H., Visker M. H. P. W., Lundén A. Selection for milk fat and milk protein composition. Advances in Animal Biosciences, 2013, vol. 4, no. 3, pp. 612–617. https://doi.org/10.1017/S2040470013000344

16. Cohen-Zinder M., Seroussi E., Larkin D. M., Loor J. J., Van der Wind A. E., Lee J.-H. [et al.]. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Research, 2005, vol. 15, no. 7, pp. 936–944. https://doi.org/10.1101/gr.3806705

17. Olsen H. G., Lien S., Gautier M., Nilsen H., Roseth A., Berg P. R., Sundsaasen K. K., Svendsen M., Meuwissen T. H. E. Mapping of a milk production quantitative trait locus to a 420-kb region on bovine chromosome 6. Genetics, 2005, vol. 169, no. 1, pp. 275–283. https://doi.org/10.1534/genetics.104.031559

18. Ron M., Cohen-Zinder M., Peter C., Weller J. I., Erhardt G. Short communication: a polymorphism in ABCG2 in Bos indicus and Bos taurus cattle breeds. Journal of Dairy Science, 2006, vol. 89, no. 12, pp. 4921–4923. https://doi.org/10.3168/jds.S0022-0302(06)72542-5

19. Blott S., Kim J. J., Moisio S., Schmidt-Küntzel A., Cornet A., Berzi P. [et al.]. Molecular dissection of a quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition. Genetics, 2003, vol. 163, no. 1, pp. 253–266. https://doi.org/10.1093/genetics/163.1.253

20. Viitala S., Szyda J., Blott S., Schulman N., Lidauer M., Mäki-Tanila A., Georges M., Vilkki J. The role of the bovine growth hormone receptor and prolactin receptor genes in milk, fat and protein production in Finnish Ayrshire dairy cattle. Genetics, 2006, vol. 173, no. 4, pp. 2151–2164. https://doi.org/10.1534/genetics.105.046730

21. Liu L., Zhou J., Chen C. J., Zhang J., Wen W., Tian J., Zhang Z., Gu Y. GWAS-based identification of new loci for milk yield, fat, and protein in Holstein cattle. Animals, 2020, vol. 10, no. 11, art. 2048. https://doi.org/10.3390/ani10112048

22. Schennink A., Stoop W. M., Visker M. H. P. W., Heck J. M., Bovenhuis H., Van der Poel J. J., Van Valenberg H. J., Van Arendonk J. A. DGAT1 underlies large genetic variation in milk-fat composition of dairy cows. Animal Genetics, 2007, vol. 38, no. 5, pp. 467–473. https://doi.org/10.1111/j.1365-2052.2007.01635.x

23. Jiang J., Ma L., Prakapenka D., VanRaden P. M., Cole J. B., Da Y. A large-scale genome-wide association study in US Holstein cattle. Frontiers in Genetics, 2019, vol. 10, art. 412. https://doi.org/10.3389/fgene.2019.00412

24. Bouwman A. C., Daetwyler H. D., Chamberlain A. J., Ponce C. H., Sargolzaei M., Schenkel F. S. [et al.]. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature Genetics, 2018, vol. 50, no. 3, pp. 362–367. https://doi.org/10.1038/s41588-018-0056-5

25. Sanchez M. P., Govignon-Gion A., Croiseau P., Fritz S., Hozé C., Miranda G. [et al.]. Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genetics Selection Evolution, 2017, vol. 49, no. 1, art. 68. https://doi.org/10.1186/s12711-017-0344-z

26. Cai Z., Guldbrandtsen B., Lund M. S., Sahana G. Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle. BMC Genomics, 2018, vol. 19, no. 1, art. 656. https://doi.org/10.1186/s12864-018-5050-x

27. Hayes B. J., Pryce J., Chamberlain A. J., Bowman P., Goddard M. E. Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits. PLoS Genetics, 2010, vol. 6, no. 9, art. e1001139. https://doi.org/10.1371/journal.pgen.1001139

28. Wiggans G. R., Cole J. B., Hubbard S. M., Sonstegard T. S. Genomic selection in dairy cattle: the USDA experience. Annual Review of Animal Biosciences, 2017, vol. 5, pp. 309–327. https://doi.org/10.1146/annurev-animal-021815-111422

29. Hayes B. J., Bowman P. J., Chamberlain A. J., Goddard M. E. Invited review: Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 2009, vol. 92, iss. 2, pp. 433–443. https://doi.org/10.3168/jds.2008-1646

30. Boichard D., Chung H., Dassonneville R., David X., Eggen A., Fritz S. [et al.]. Design of a bovine low-density SNP array optimized for imputation. PLoS One, 2012, vol. 7, no. 3, art. e34130. https://doi.org/10.1371/journal.pone.0034130

31. Sharma A., Lee J. S., Dang C. G., Sudrajad P., Kim H. C., Yeon S. H., Kang H. S., Lee S.-H. Stories and challenges of genome wide association studies in livestock – a review. Asian-Australasian Journal of Animal Sciences, 2015, vol. 28, no. 10, pp. 1371–1379. https://doi.org/10.5713/ajas.14.0715

32. Lund M. S., Van den Berg I., Ma P., Brøndum R. F., Su G. Review: How to improve genomic predictions in small dairy cattle populations. Animal, 2016, vol. 10, no. 11, pp. 1042–1049. https://doi.org/10.1017/S1751731115003031

33. Hu Z. L., Park C. A., Reecy J. M. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB. Nucleic Acids Research, 2019, vol. 47, iss. D1, pp. D701–D710. https://doi.org/10.1093/nar/gky1084

34. Sayers E. W., Beck J., Bolton E. E., Bourexis D., Brister J. R., Canese K. [et al.]. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 2021, vol. 49, iss. D1, pp. D10–D17. https://doi.org/10.1093/nar/gkaa892

35. Evangelou E., Ioannidis J. P. A. Meta-analysis methods for genome-wide association studies and beyond. Nature Reviews Genetics, 2013, vol. 14, no. 6, pp. 379–389. https://doi.org/10.1038/nrg3472

36. Fornes O., Castro-Mondragon J. A., Khan A., Van der Lee R., Zhang X., Richmond P. A. [et al.]. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Research, 2020, vol. 48, iss. D1, pp. D87–D92. https://doi.org/10.1093/nar/gkz1001

37. Daetwyler H. D., Capitan A., Pausch H., Stothard P., Van Binsbergen R., Brøndum R. F. [et al.]. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics, 2014, vol. 46, no. 8, pp. 858–865. https://doi.org/10.1038/ng.3034

38. Viechtbauer W. Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 2010, vol. 36, no. 3, pp. 1–48. https://doi.org/10.18637/jss.v036.i03

39. DerSimonian R., Laird N. Meta-analysis in clinical trials. Controlled Clinical Trials, 1986, vol. 7, no. 3, pp. 177–188. https://doi.org/10.1016/0197-2456(86)90046-2